Coherent Point Drift Peak Alignment Algorithms Using Distance and Similarity Measures for Two-Dimensional Gas...

Area of Science:

• Analytical Chemistry
• Metabolomics
• Mass Spectrometry

Background:

• Peak alignment is crucial for analyzing two-dimensional gas chromatography mass spectrometry (2DGC-MS) metabolomics data.
• Experimental variations cause retention time shifts, complicating accurate peak alignment.
• Existing local alignment algorithms struggle with dense biological data, leading to low accuracy.

Purpose of the Study:

• To develop novel global peak alignment (GPA) algorithms for 2DGC-MS metabolomics data.
• To address the limitations of existing local alignment methods, particularly for complex datasets.
• To improve the accuracy of peak alignment for enhanced downstream analysis.

Main Methods:

• Developed four global peak alignment (GPA) algorithms based on coherent point drift (CPD) point matching.
• Algorithms include retention time-based CPD-GPA (RT), prior CPD-GPA (P), mixture CPD-GPA (M), and prior mixture CPD-GPA (P+M).
• Methods P, M, and P+M incorporate mass spectral similarity alongside retention time for alignment.

Main Results:

• The developed CPD-GPA algorithms were applied to homogeneous, heterogeneous, and real biological 2DGC-MS data.
• Performance was compared against existing algorithms: mSPA, SWPA, and BiPACE-2D.
• CPD-GPA algorithms demonstrated superior performance, achieving higher F1 scores across all tested datasets.

Conclusions:

• The novel CPD-GPA algorithms provide a more accurate and robust solution for peak alignment in 2DGC-MS metabolomics.
• Global alignment approach effectively handles retention time shifts and improves data comparability.
• Enhanced peak alignment accuracy facilitates more reliable biomarker discovery and pathway analysis.